Shopify is an e-commerce platform that allows small and growing businesses to create and manage their online stores. Shopify handles a wide variety of services, from payment processing to storefront creation to logistics to SEO.
When you’re preparing for an interview at Shopify, it is crucial to grasp their unique process. At Shopify, your interview will involve an interaction with the team recruiter, highly technical Shopify interview questions with a senior employee, a problem-solving session, and a potential deep dive into one of your past projects. The primary focus of the interviews is on your technical skills and problem-solving approach. Let’s break down their interview process.
Here are the rounds you should expect to go through while interviewing at Shopify:
Throughout the process, keep in mind that Shopify values real work scenarios and doesn’t rely on trivia or brain teasers. Your ultimate goal should be to demonstrate your potential as a part of their team and your capacity to add value to the company.
Behavioral interview questions are a significant part of the Shopify interview process. A huge portion of their interview journey is focused on identifying whether you are a good culture fit. Here are some behavioral questions that will definitely come up:
Shopify Life Story Interview
The Life Story interview is typically conducted by a recruiter or hiring manager, and it is an open conversation that doesn’t follow any rigid structure. The interviewer will ask you questions about your life, your values, your goals, and your experiences. They may also ask you to share stories about times when you have faced challenges or overcome obstacles.
The goal of the Life Story interview is to get to know you as a person and to understand how your past experiences have shaped you. Shopify is looking for people who are passionate, driven, and who have a strong sense of purpose. They also want to find people who fit their company culture.
If you are invited to a Life Story interview, be prepared to share your personal story. This is your chance to show the interviewer who you are and why you would be a good fit for Shopify. Be honest, be authentic, and be yourself.
Shopify Past Experiences Interview
In the Past Experiences interview, Shopify seeks to understand your professional journey and how your past roles and projects align with the role you are applying for. Interviewers will want to hear about your hands-on experience with data analysis, predictive modeling, machine learning algorithms, or any other relevant skills.
Shopify values continuous learning and curiosity. If you’ve taught yourself a new programming language, mastered a complex statistical model, or found an innovative solution to a thorny problem, those are stories worth sharing.
Shopify Values and Culture Fit Interview
The Values and Culture Fit interview is designed to understand how you align with Shopify’s core values and culture. Shopify is looking for individuals who are not just skilled but also share the company’s commitment to long-term merchant success and constant learning.
You may be asked about how you’ve dealt with failure in the past, how you’ve contributed to team success, or how you embody a growth mindset. The purpose is to assess your personal qualities and whether you’ll thrive in Shopify’s fast-paced, merchant-oriented environment.
As you prepare for this interview, take the time to understand Shopify’s mission and values. Reflect on how these resonate with you and be prepared to provide examples from your own experience that illustrate your alignment with these values.
Remember, these interviews are not just for Shopify to learn about you, but also for you to learn about Shopify. Come prepared with thoughtful questions about the company, the team, and the role. This shows your genuine interest in the position and can help you decide if Shopify is the right place for you.
Shopify as a company isn’t too fond of asking algorithmic questions but instead focuses on practical coding questions that you will encounter when in the role. Many of those that interview at Shopify note their “alternative” approach to interviewing.
Coding-type questions appear in 97% of their job interviews, and focus on rather practical topics, but they also focus on Object-Oriented Programming, Data Structures, and Programming Principles. Here are some questions that are likely to be asked in your Shopify interviews:
Given an array of integers, write a function move_zeros_back
that moves all zeros in the array to the end of the array. If there are no zeros, return the input array.
Priority queues are an abstract data structure responsible for allowing enqueuing items with an attached priority. This priority determines the order in which elements are dequeued, with higher-priority elements typically dequeued before lower-priority ones. Most implementations of priority queues are built upon the heap data structure due to its efficient performance. However, for the purpose of this exercise, implement a priority queue using a traditional singly linked list.
You have been tasked with designing three classes: text_editor
, moving_text_editor
, and smart_text_editor
. These classes are to be created with specific functionalities.
To practice Coding interview questions, consider using the Python learning path or the full list of Coding questions in our database.
Case studies are an important part of Shopify job interviews. Many interviewees report that Shopify has a tendency to ask practical, on-the-job questions. Typically, case study interview questions appear in 70% of Shopify job interviews. Here are some that you might encounter:
Your manager has run an A/B test with 20 different variants and claims that one of the variants is significant. Evaluate the situation and discuss if there’s anything questionable about the results.
You work at an eCommerce startup and run an A/B test on the company’s checkout product page with the hypothesis that surfacing free shipping will increase conversions. The experiment group specifies whether the product qualifies for free shipping or not, while the control group does not. After running the experiment for two weeks, you have certain results. Analyze these results to determine if the test was successful.
An e-commerce company has been experiencing a reduction in revenue for the past 12 months. You have access to various transaction data. Analyze this dataset to pinpoint exactly where the revenue loss is occurring.
An e-commerce company has been experiencing a reduction in revenue for the past 12 months. You have the following transaction data: Date of sale, Total $ Amount paid by the customer, Profit Margin per unit, Quantity of item, Item category, Item subcategory, Marketing attribution source, and % discount applied). How would you analyze this dataset to pinpoint where the revenue loss is occurring?
For case studies, you can utilize the product metrics learning path and the data analytics learning path to enhance your understanding and application of these concepts.
Creating SQL queries is an integral part of the job, whether you’re a data analyst, data engineer, data scientist, or software engineer. Shopify, however, is not too keen on asking these types of questions, even on DS-related roles. Here are some SQL practice questions to prepare you in the areas that Shopify might still be interested in.
Assume you work at Costco with a database containing two tables: users
and purchases
. users
contains user information, including their registration date, and purchases
contains the entire item purchase history for those users. Write a query to calculate the total amount spent on each item in the purchases
table by users who registered in 2022.
Given a table of product purchases where each row represents an individual user product purchase, write a query to determine the number of users who bought additional products after their first purchase. Note that if a customer purchased two things on the same day, it does not count as an upsell.
Given a table of bank transactions with columns for user_id
, a deposit or withdrawal value, and created_at
time for each transaction, write a query to calculate the total three-day rolling average for deposits by day. The date in the output should be in the format '%Y-%m-%d'
.
To continue practicing, try the SQL learning path and the full list of SQL questions and solutions in our interview questions database.
As rare as SQL questions are in Shopify job interviews, machine learning questions appear even more sparsely. Appearing in only one in twenty interviews, here are some that you might recognize:
You are working on keyword bidding optimization. You have a dataset with keywords being bid against and the price paid for those keywords. How would you use this data to build a model that can bid on a new, unseen keyword?
As a data scientist working on pricing products on an ecommerce site, you discover our algorithm is vastly under-pricing a certain consumer product. The online price is dependent on product availability, demand, and logistics cost. What steps would you take to diagnose this problem?
You are developing a fraud detection system for Bank of America. The system monitors credit card activities and flags a transaction as suspicious if it happened in a place other than where a person lived. How would you infer a customer’s location from their purchases to make this system work?
To get ready for machine learning interview questions, we recommend taking the machine learning course.
Design patterns are critical for making software scalable, maintainable, and easy to understand. These patterns refer to general repeatable solutions to common problems that occur during software design. Here are some key design patterns that Shopify uses or that could come up during the interview:
1. Factory Pattern: Factory pattern is one of the most used design patterns. This type of design pattern comes under the creational pattern as it provides one of the best ways to create an object. The factory method lets a class defer instantiation to subclasses.
2. Observer Pattern: This pattern is used when there is one-to-many relationship between objects. If one object is modified, its dependent objects are to be notified automatically.
3. Model-View-Controller (MVC) Pattern: This is a pattern used to separate application’s concerns. Model represents an object carrying data. View represents the visualization of the data that the model contains. The controller acts on both model and view. It controls the data flow into the model object and updates the view whenever data changes.
4. Object-Oriented Programming (OOP): Object-oriented programming is a paradigm that organizes software design around data, or objects, rather than functions and logic. It is heavily reliant on the four pillars of OOP: Inheritance, Encapsulation, Polymorphism, and Abstraction. Make sure to know what these pillars signify and what are the best practices in implementing them.
When it comes to best practices, Shopify upholds several of them, notably the practice of “Pair Programming”. This practice involves two programmers sharing one workstation (one screen, keyboard and mouse among the pair). The programmer at the keyboard is usually called the “driver”, the other, also actively involved in the programming task but focusing more on overall direction and on the “big picture”, is often called the “navigator”.
Pair programming is a core element of extreme programming (XP), a pragmatic methodology of software development that takes a ‘whole team’ approach to business software development. The practice increases code quality by making the development process more engaging for programmers, and it encourages knowledge sharing and team building.
In addition to pair programming, Shopify also encourages code reviews, continuous integration, and automated testing, all of which are designed to maintain a high standard of code quality. It’s important to familiarize yourself with these best practices, as they may be discussed during the interview process.
Most data science positions fall under different position titles depending on the actual role.
From the graph we can see that on average the Business Intelligence role pays the most with a $133,000 base salary while the Data Analyst role on average pays the least with a $85,000 base salary.